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Multiprojection Correlation Imaging for Improved Detection of Pulmonary Nodules

Ehsan Samei1,2,3, Stanton A. Stebbins1, James T. Dobbins, III1,3 and Joseph Y. Lo1,3

1 Duke Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Rd., Suite 302, Durham, NC 27705.
2 Department of Physics, Duke University Medical Center, Durham, NC.
3 Department of Biomedcial Engineering, Duke University Medical Center, Durham, NC.


Figure 1
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  		Fig. 1 —Diagram shows configuration used to acquire projection images in correlation imaging. X-ray tube moves precisely along vertical axis to acquire projection images from required angle ({theta}). Displacement of lesion in each projection image is defined by angle, source-to-image distance (S), and distance (d) between nodule (or nodule phantom) and detector.

 

Figure 2
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  		Fig. 2 —Flowchart shows computer-aided detection correlation imaging algorithm. DOG = difference of gaussians.

 

Figure 3
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  		Fig. 3 —Image shows sample difference-of-gaussians (DOG) filter output from anthropomorphic phantom projection. Bright areas represent areas of greatest similarity between input image and DOG filter output.

 

Figure 4
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  		Fig. 4 —60-year-old man with pulmonary nodules. Final prethresholding contour map is produced with correlation imaging algorithm generated by shift-and-add tomosynthesis reconstruction of contour maps produced from each projection as input, summation of all slices to produce 2D image, and application of manual lung field segmentation. Bright areas represent areas where nodule is likely to be found.

 

Figure 5
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  		Fig. 5A —Anthropomorphic phantom. Sample projection posteroanterior image (no angular offset).

 

Figure 6
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  		Fig. 5B —Anthropomorphic phantom. Sample postthresholding output for correlation imaging algorithm. Red regions represent possible nodules detected with correlation imaging. Circles indicate nodules in truth file. Possible nodules that intersect circles are counted as true-positive findings, and those that do not are counted as false-positive findings.

 

Figure 7
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  		Fig. 6A —Analysis of false-positive findings for phantom. Graph shows free-response receiver operating characteristic (FROC) results for correlation imaging in differing numbers of anthropomorphic phantom projections along with FROC results for 2D computer-aided detection (2D CAD) output of single posteroanterior projection.

 

Figure 8
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  		Fig. 6B —Analysis of false-positive findings for phantom. Graph with fixed sensitivity level of 65% shows relation between number of false-positive findings and number of images used in correlation imaging algorithm.

 

Figure 9
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  		Fig. 7A —60-year-old man with pulmonary nodules. Sample projection image in posteroanterior orientation (no angular offset).

 

Figure 10
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  		Fig. 7B —60-year-old man with pulmonary nodules. Output for correlation imaging algorithm at threshold of 66% sensitivity. Red regions represent possible nodules detected with correlation imaging. Circles represent nodules in truth file. Possible nodules that intersect circles are counted as true-positive findings, and those that do not are counted as false-positive findings.

 

Figure 11
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  		Fig. 8A —Analysis of false-positive findings for human subject. Graph shows free-response receiver operating characteristic (FROC) results for correlation imaging with differing numbers of human subject projections along with FROC for 2D computer-aided detection (2D CAD) output of single posteroanterior view.

 

Figure 12
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  		Fig. 8B —Analysis of false-positive findings for human subject. Graph with fixed sensitivity level of 65% shows relation between number of false-positive findings and number of images used in correlation imaging algorithm.

 

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